{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# 9 Pandas分组与聚合"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 分组运算"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-26T06:41:37.407122Z",
     "start_time": "2025-02-26T06:41:37.038150Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "#分组后给名称加前缀\n",
    "dict_obj = {'key1' : ['a', 'b', 'a', 'b',\n",
    "                      'a', 'b', 'a', 'a'],\n",
    "            'key2' : ['one', 'one', 'two', 'three',\n",
    "                      'two', 'two', 'one', 'three'],\n",
    "            'data1': np.random.randint(1, 10, 8),\n",
    "            'data2': np.random.randint(1, 10, 8)}\n",
    "df_obj = pd.DataFrame(dict_obj)\n",
    "print(df_obj)\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  key1   key2  data1  data2\n",
      "0    a    one      2      5\n",
      "1    b    one      7      1\n",
      "2    a    two      7      3\n",
      "3    b  three      9      4\n",
      "4    a    two      5      2\n",
      "5    b    two      1      3\n",
      "6    a    one      8      1\n",
      "7    a  three      8      9\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-26T06:44:01.080356Z",
     "start_time": "2025-02-26T06:44:01.073526Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print('-'*50)\n",
    "# 按key1分组后，计算data1，data2的统计信息并附加到原始表格中，并添加表头前缀\n",
    "k1_sum = df_obj.groupby('key1').mean(numeric_only=True).add_prefix('mean_')\n",
    "print(k1_sum)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "      mean_data1  mean_data2\n",
      "key1                        \n",
      "a       6.000000    4.000000\n",
      "b       5.666667    2.666667\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-26T06:51:01.871139Z",
     "start_time": "2025-02-26T06:51:01.863551Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 方法2，使用transform，分组后计算结果和原本的df保持一致\n",
    "k1_sum_tf = df_obj.loc[:, ['key1','data1', 'data2']].groupby('key1').transform('mean').add_prefix('mean_')\n",
    "k1_sum_tf"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   mean_data1  mean_data2\n",
       "0    6.000000    4.000000\n",
       "1    5.666667    2.666667\n",
       "2    6.000000    4.000000\n",
       "3    5.666667    2.666667\n",
       "4    6.000000    4.000000\n",
       "5    5.666667    2.666667\n",
       "6    6.000000    4.000000\n",
       "7    6.000000    4.000000"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_data1</th>\n",
       "      <th>mean_data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>2.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>2.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>2.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-26T06:46:23.766174Z",
     "start_time": "2025-02-26T06:46:23.761453Z"
    }
   },
   "cell_type": "code",
   "source": "df_obj.groupby('key1').mean()",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         data1     data2\n",
       "key1                    \n",
       "a     6.000000  4.000000\n",
       "b     5.666667  2.666667"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>2.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "df_obj"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "del df_obj['key2']"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "df_obj.groupby('key1').transform(np.mean)"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-26T06:49:40.880083Z",
     "start_time": "2025-02-26T06:49:40.873079Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#实现a组和b组，谁比平均分高，谁比平均分低\n",
    "def diff_mean(s):\n",
    "    \"\"\"\n",
    "        返回数据与均值的差值，s传入的是某一个分组\n",
    "    \"\"\"\n",
    "    return s - s.mean()\n",
    "\n",
    "print(df_obj.loc[:, ['key1','data1', 'data2']].groupby('key1').transform(diff_mean))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      data1     data2\n",
      "0 -4.000000  1.000000\n",
      "1  1.333333 -1.666667\n",
      "2  1.000000 -1.000000\n",
      "3  3.333333  1.333333\n",
      "4 -1.000000 -2.000000\n",
      "5 -4.666667  0.333333\n",
      "6  2.000000 -3.000000\n",
      "7  2.000000  5.000000\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "source": "# 设置索引（放到最后讲解）",
   "metadata": {
    "collapsed": false
   }
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "   a  b    c  d\n0  0  7  one  h\n1  1  6  one  j\n2  2  5  one  k\n3  3  4  two  l\n4  4  3  two  m\n5  5  2  two  n\n6  6  1  two  o",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>7</td>\n      <td>one</td>\n      <td>h</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>6</td>\n      <td>one</td>\n      <td>j</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>5</td>\n      <td>one</td>\n      <td>k</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>4</td>\n      <td>two</td>\n      <td>l</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>3</td>\n      <td>two</td>\n      <td>m</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>2</td>\n      <td>two</td>\n      <td>n</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6</td>\n      <td>1</td>\n      <td>two</td>\n      <td>o</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#索引中单项不可变，但是整体可以换掉\n",
    "a = pd.DataFrame({'a': range(7),'b': range(7, 0, -1),\n",
    "                  'c': ['one','one','one','two','two','two', 'two'],\n",
    "                  'd': list(\"hjklmno\")})\n",
    "a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-11T08:45:10.277156100Z",
     "start_time": "2024-04-11T08:45:10.231181600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b    c  d\n",
      "0  0  7  one  h\n",
      "1  1  6  one  j\n",
      "2  2  5  one  k\n",
      "3  3  4  two  l\n",
      "4  4  3  two  m\n",
      "5  5  2  two  n\n",
      "6  6  1  two  o\n",
      "--------------------------------------------------\n",
      "   a  b    c  d\n",
      "a  0  7  one  h\n",
      "b  1  6  one  j\n",
      "c  2  5  one  k\n",
      "d  3  4  two  l\n",
      "e  4  3  two  m\n",
      "f  5  2  two  n\n",
      "g  6  1  two  o\n"
     ]
    }
   ],
   "source": [
    "c=a.copy()\n",
    "a.index=list('abcdefg')  #a的索引变了，a.index更换索引\n",
    "print(c)\n",
    "# a.columns=list('ABCD')  #a的列名变了，a.columns更换列名\n",
    "print('-'*50)\n",
    "print(a)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-11T08:40:17.872603300Z",
     "start_time": "2024-04-11T08:40:17.805642100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "(7, 4)"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.values.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-11T08:42:05.057905400Z",
     "start_time": "2024-04-11T08:42:05.034914200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b    c  d\n",
      "0  0  7  one  h\n",
      "1  1  6  one  j\n",
      "2  2  5  one  k\n",
      "3  3  4  two  l\n",
      "4  4  3  two  m\n",
      "5  5  2  two  n\n",
      "6  6  1  two  o\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": "       a  b\nc   d      \none h  0  7\n    j  1  6\n    k  2  5\ntwo l  3  4\n    m  4  3\n    n  5  2\n    o  6  1",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n    </tr>\n    <tr>\n      <th>c</th>\n      <th>d</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">one</th>\n      <th>h</th>\n      <td>0</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>j</th>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>k</th>\n      <td>2</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">two</th>\n      <th>l</th>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>m</th>\n      <td>4</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>n</th>\n      <td>5</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>o</th>\n      <td>6</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#让某些列变为索引，让c列，d列数据变为索引\n",
    "print(a)\n",
    "print('-'*50)\n",
    "a.set_index(['c','d'],inplace=True)#a没变，返回修改后的df\n",
    "a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-11T08:45:16.370494800Z",
     "start_time": "2024-04-11T08:45:16.345501300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 时间序列"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',\n               '2019-01-05', '2019-01-06', '2019-01-07', '2019-01-08',\n               '2019-01-09', '2019-01-10', '2019-01-11', '2019-01-12',\n               '2019-01-13', '2019-01-14', '2019-01-15', '2019-01-16',\n               '2019-01-17', '2019-01-18', '2019-01-19', '2019-01-20',\n               '2019-01-21', '2019-01-22', '2019-01-23', '2019-01-24',\n               '2019-01-25', '2019-01-26', '2019-01-27', '2019-01-28',\n               '2019-01-29', '2019-01-30', '2019-01-31', '2019-02-01'],\n              dtype='datetime64[ns]', freq='D')"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20190101\", end=\"20190201\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-11T08:47:12.099623900Z",
     "start_time": "2024-04-11T08:47:12.028664400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2024-04-11', '2024-04-12', '2024-04-15', '2024-04-16',\n               '2024-04-17', '2024-04-18', '2024-04-19', '2024-04-22',\n               '2024-04-23', '2024-04-24'],\n              dtype='datetime64[ns]', freq='B')"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20240411\",periods=10,freq='B')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-11T08:49:08.593194300Z",
     "start_time": "2024-04-11T08:49:08.521236100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2019-01-31', '2019-02-28', '2019-03-31', '2019-04-30',\n               '2019-05-31', '2019-06-30', '2019-07-31', '2019-08-31',\n               '2019-09-30', '2019-10-31'],\n              dtype='datetime64[ns]', freq='M')"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20190101\",periods=10,freq='M')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-11T08:49:21.333919500Z",
     "start_time": "2024-04-11T08:49:21.289071500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',\n               '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',\n               '2019-09-01', '2019-10-01'],\n              dtype='datetime64[ns]', freq='MS')"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20190101\",periods=10,freq='MS')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-11T08:49:32.430072Z",
     "start_time": "2024-04-11T08:49:32.392088400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2023-07-16', '2023-07-23', '2023-07-30', '2023-08-06',\n               '2023-08-13', '2023-08-20', '2023-08-27', '2023-09-03',\n               '2023-09-10', '2023-09-17'],\n              dtype='datetime64[ns]', freq='W-SUN')"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20230710\",periods=10,freq='W')  #拿每周的周日生成"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "0     3/11/2000\n1     3/12/2000\n2     3/13/2000\n3     3/11/2000\n4     3/12/2000\n5     3/13/2000\n6     3/11/2000\n7     3/12/2000\n8     3/13/2000\n9     3/11/2000\n10    3/12/2000\n11    3/13/2000\n12    3/11/2000\n13    3/12/2000\n14    3/13/2000\ndtype: object"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 5)\n",
    "s"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-11T08:51:00.948348400Z",
     "start_time": "2024-04-11T08:51:00.902375200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "0    2000-03-11\n1    2000-03-12\n2    2000-03-13\n3    2000-03-11\n4    2000-03-12\n5    2000-03-13\n6    2000-03-11\n7    2000-03-12\n8    2000-03-13\n9    2000-03-11\n10   2000-03-12\n11   2000-03-13\n12   2000-03-11\n13   2000-03-12\n14   2000-03-13\ndtype: datetime64[ns]"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#timeit可以统计执行耗时，to_datetime把字符串转为时间格式\n",
    "pd.to_datetime(s, infer_datetime_format=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-11T08:51:42.025392300Z",
     "start_time": "2024-04-11T08:51:41.992410100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           lat        lng  \\\n",
      "timeStamp                                   \n",
      "2015-12-10 17:10:52  40.297876 -75.581294   \n",
      "2015-12-10 17:29:21  40.258061 -75.264680   \n",
      "2015-12-10 14:39:21  40.121182 -75.351975   \n",
      "2015-12-10 16:47:36  40.116153 -75.343513   \n",
      "2015-12-10 16:56:52  40.251492 -75.603350   \n",
      "2015-12-10 15:39:04  40.253473 -75.283245   \n",
      "2015-12-10 16:46:48  40.182111 -75.127795   \n",
      "2015-12-10 16:17:05  40.217286 -75.405182   \n",
      "2015-12-10 16:51:42  40.289027 -75.399590   \n",
      "2015-12-10 17:35:41  40.102398 -75.291458   \n",
      "\n",
      "                                                                  desc  \\\n",
      "timeStamp                                                                \n",
      "2015-12-10 17:10:52  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
      "2015-12-10 17:29:21  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
      "2015-12-10 14:39:21  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
      "2015-12-10 16:47:36  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
      "2015-12-10 16:56:52  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
      "2015-12-10 15:39:04  CANNON AVE & W 9TH ST;  LANSDALE; Station 345;...   \n",
      "2015-12-10 16:46:48  LAUREL AVE & OAKDALE AVE;  HORSHAM; Station 35...   \n",
      "2015-12-10 16:17:05  COLLEGEVILLE RD & LYWISKI RD;  SKIPPACK; Stati...   \n",
      "2015-12-10 16:51:42  MAIN ST & OLD SUMNEYTOWN PIKE;  LOWER SALFORD;...   \n",
      "2015-12-10 17:35:41  BLUEROUTE  & RAMP I476 NB TO CHEMICAL RD; PLYM...   \n",
      "\n",
      "                         zip                        title                twp  \\\n",
      "timeStamp                                                                      \n",
      "2015-12-10 17:10:52  19525.0       EMS: BACK PAINS/INJURY        NEW HANOVER   \n",
      "2015-12-10 17:29:21  19446.0      EMS: DIABETIC EMERGENCY  HATFIELD TOWNSHIP   \n",
      "2015-12-10 14:39:21  19401.0          Fire: GAS-ODOR/LEAK         NORRISTOWN   \n",
      "2015-12-10 16:47:36  19401.0       EMS: CARDIAC EMERGENCY         NORRISTOWN   \n",
      "2015-12-10 16:56:52      NaN               EMS: DIZZINESS   LOWER POTTSGROVE   \n",
      "2015-12-10 15:39:04  19446.0             EMS: HEAD INJURY           LANSDALE   \n",
      "2015-12-10 16:46:48  19044.0         EMS: NAUSEA/VOMITING            HORSHAM   \n",
      "2015-12-10 16:17:05  19426.0   EMS: RESPIRATORY EMERGENCY           SKIPPACK   \n",
      "2015-12-10 16:51:42  19438.0        EMS: SYNCOPAL EPISODE      LOWER SALFORD   \n",
      "2015-12-10 17:35:41  19462.0  Traffic: VEHICLE ACCIDENT -           PLYMOUTH   \n",
      "\n",
      "                                                         addr  e     cate  \n",
      "timeStamp                                                                  \n",
      "2015-12-10 17:10:52                    REINDEER CT & DEAD END  1      EMS  \n",
      "2015-12-10 17:29:21                BRIAR PATH & WHITEMARSH LN  1      EMS  \n",
      "2015-12-10 14:39:21                                  HAWS AVE  1     Fire  \n",
      "2015-12-10 16:47:36                        AIRY ST & SWEDE ST  1      EMS  \n",
      "2015-12-10 16:56:52                  CHERRYWOOD CT & DEAD END  1      EMS  \n",
      "2015-12-10 15:39:04                     CANNON AVE & W 9TH ST  1      EMS  \n",
      "2015-12-10 16:46:48                  LAUREL AVE & OAKDALE AVE  1      EMS  \n",
      "2015-12-10 16:17:05              COLLEGEVILLE RD & LYWISKI RD  1      EMS  \n",
      "2015-12-10 16:51:42             MAIN ST & OLD SUMNEYTOWN PIKE  1      EMS  \n",
      "2015-12-10 17:35:41  BLUEROUTE  & RAMP I476 NB TO CHEMICAL RD  1  Traffic  \n"
     ]
    }
   ],
   "source": [
    "#实战美国911数据\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# 把时间字符串转为时间类型设置为索引\n",
    "df = pd.read_csv(\"./911.csv\")\n",
    "df[\"timeStamp\"] = pd.to_datetime(df[\"timeStamp\"])\n",
    "\n",
    "# 添加列，表示分类\n",
    "temp_list = df[\"title\"].str.split(\": \").tolist() #二维列表\n",
    "cate_list = [i[0] for i in temp_list] #i[0]就是EMS  Fire  Traffic\n",
    "# print(cate_list)\n",
    "# print(np.array(cate_list).reshape((df.shape[0], 1)))\n",
    " #添加一列\n",
    "df[\"cate\"] = pd.DataFrame(np.array(cate_list).reshape((df.shape[0], 1)))\n",
    "\n",
    "df.set_index(\"timeStamp\", inplace=True) #设置索引，时间戳，inplace=True表示在原df上修改\n",
    "\n",
    "print(df.head(10))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timeStamp\n",
      "2015-12-31    3898\n",
      "2016-01-31    6063\n",
      "2016-02-29    5413\n",
      "2016-03-31    5832\n",
      "2016-04-30    5684\n",
      "2016-05-31    5730\n",
      "2016-06-30    5720\n",
      "2016-07-31    6029\n",
      "2016-08-31    6005\n",
      "2016-09-30    5750\n",
      "2016-10-31    6039\n",
      "2016-11-30    5838\n",
      "2016-12-31    6024\n",
      "2017-01-31    6082\n",
      "2017-02-28    5417\n",
      "2017-03-31    5913\n",
      "2017-04-30    5780\n",
      "2017-05-31    5948\n",
      "2017-06-30    6030\n",
      "2017-07-31    5974\n",
      "2017-08-31    5882\n",
      "2017-09-30    3789\n",
      "Freq: M, Name: title, dtype: int64\n",
      "DatetimeIndex(['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31',\n",
      "               '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31',\n",
      "               '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30',\n",
      "               '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31',\n",
      "               '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31',\n",
      "               '2017-08-31', '2017-09-30'],\n",
      "              dtype='datetime64[ns]', name='timeStamp', freq='M')\n",
      "timeStamp\n",
      "2015-12-31    1095\n",
      "2016-01-31    1904\n",
      "2016-02-29    1868\n",
      "2016-03-31    1589\n",
      "2016-04-30    1717\n",
      "2016-05-31    1573\n",
      "2016-06-30    1787\n",
      "2016-07-31    1898\n",
      "2016-08-31    1907\n",
      "2016-09-30    1793\n",
      "2016-10-31    1930\n",
      "2016-11-30    1765\n",
      "2016-12-31    1846\n",
      "2017-01-31    1658\n",
      "2017-02-28    1462\n",
      "2017-03-31    1634\n",
      "2017-04-30    1614\n",
      "2017-05-31    1670\n",
      "2017-06-30    1986\n",
      "2017-07-31    1754\n",
      "2017-08-31    1862\n",
      "2017-09-30    1120\n",
      "Freq: M, Name: title, dtype: int64\n",
      "DatetimeIndex(['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31',\n",
      "               '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31',\n",
      "               '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30',\n",
      "               '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31',\n",
      "               '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31',\n",
      "               '2017-08-31', '2017-09-30'],\n",
      "              dtype='datetime64[ns]', name='timeStamp', freq='M')\n",
      "timeStamp\n",
      "2015-12-31    2923\n",
      "2016-01-31    5129\n",
      "2016-02-29    4115\n",
      "2016-03-31    3638\n",
      "2016-04-30    3886\n",
      "2016-05-31    4071\n",
      "2016-06-30    4225\n",
      "2016-07-31    4161\n",
      "2016-08-31    3992\n",
      "2016-09-30    4126\n",
      "2016-10-31    4533\n",
      "2016-11-30    4488\n",
      "2016-12-31    4292\n",
      "2017-01-31    3865\n",
      "2017-02-28    3388\n",
      "2017-03-31    4137\n",
      "2017-04-30    3662\n",
      "2017-05-31    4101\n",
      "2017-06-30    4317\n",
      "2017-07-31    4040\n",
      "2017-08-31    4009\n",
      "2017-09-30    2367\n",
      "Freq: M, Name: title, dtype: int64\n",
      "DatetimeIndex(['2015-12-31', '2016-01-31', '2016-02-29', '2016-03-31',\n",
      "               '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31',\n",
      "               '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30',\n",
      "               '2016-12-31', '2017-01-31', '2017-02-28', '2017-03-31',\n",
      "               '2017-04-30', '2017-05-31', '2017-06-30', '2017-07-31',\n",
      "               '2017-08-31', '2017-09-30'],\n",
      "              dtype='datetime64[ns]', name='timeStamp', freq='M')\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8), dpi=80)\n",
    "\n",
    "# 分组,一图多线\n",
    "#resample降采样，按月统计,索引必须是时间类型，类似groupby\n",
    "for group_name, group_data in df.groupby(by=\"cate\"):\n",
    "    # 对不同的分类都进行绘图\n",
    "    count_by_month = group_data.resample(\"M\").count()[\"title\"]  #降采样\n",
    "    print(count_by_month)\n",
    "    # 画图\n",
    "    _x = count_by_month.index\n",
    "    print(_x)\n",
    "    _y = count_by_month.values #values是对应事故发生次数\n",
    "\n",
    "    _x = [i.strftime(\"%Y%m%d\") for i in _x]  #变年月日格式\n",
    "\n",
    "    plt.plot(_x, _y, label=group_name)\n",
    "\n",
    "plt.xticks(range(len(_x)), _x, rotation=45)\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
